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The possibilities to support ZB GIS®database update using object-based image analysis in ecognition developer software

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Geodezija ir Kartografija 2014年第3期40卷 122-132页

The process of updating a spatial database is a necessary part of database management in order to keep the stored information of an acceptable quality. The first step of database update requires change detection. Many methods have been suggested to detect changes, mostly pixel-based. Recently, with the spread of very high resolution images and object-based image analysis, object-based methods were developed, too. This article presents object-based change detection method for update of the vector database ZB GIS®, that is a geometric base of the Slovak National infrastructure of spatial information, using orthophoto of the area of interest. The method stages include the following: segmentation of orthophoto using the geometry of database objects and further according to spectral and spatial information, classification according to ZB GIS®, defining reclassification rules between two classes. The proposed method was tested in two localities – Malženice (agricultural landscape) and Chopok-Jasná (mountainous landscape) reaching the overall accuracy of classification 87.12% and 84.55%, respectively. The main limitation of the method is that it can be applied only for polygonal objects.

关键词： remote sensing change detection object-based image analysis object-based classification spatial database database update

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Automated spine and vertebrae detection in CT images using object-based image analysis

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INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN BIOMEDICAL ENGINEERING 2013年第9期29卷 938-963页

作者： Schwier, M. Chitiboi, T. Huelnhagen, T. Hahn, H. K. Fraunhofer MEVIS Inst Med Image Comp Bremen Germany

Although computer assistance has become common in medical practice, some of the most challenging tasks that remain unsolved are in the area of automatic detection and recognition. The human visual perception is in general far superior to computer vision algorithms. object-based image analysis is a relatively new approach that aims to lift image analysis from a pixel-based processing to a semantic region-based processing of images. It allows effective integration of reasoning processes and contextual concepts into the recognition method. In this paper, we present an approach that applies object-based image analysis to the task of detecting the spine in computed tomography images. A spine detection would be of great benefit in several contexts, from the automatic labeling of vertebrae to the assessment of spinal pathologies. We show with our approach how region-based features, contextual information and domain knowledge, especially concerning the typical shape and structure of the spine and its components, can be used effectively in the analysis process. The results of our approach are promising with a detection rate for vertebral bodies of 96% and a precision of 99%. We also gain a good two-dimensional segmentation of the spine along the more central slices and a coarse three-dimensional segmentation. Copyright (c) 2013 John Wiley & Sons, Ltd.

关键词： spine detection vertebrae detection object-based image analysis

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Assessing and monitoring semi-arid shrublands using object-based image analysis and multiple endmember spectral mixture analysis

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ENVIRONMENTAL MONITORING AND ASSESSMENT 2013年第4期185卷 3173-3190页

作者： Hamada, Yuki Stow, Douglas A. Roberts, Dar A. Franklin, Janet Kyriakidis, Phaedon C. Argonne Natl Lab Argonne IL 60439 USA San Diego State Univ Dept Geog San Diego CA 92182 USA Univ Calif Santa Barbara Dept Geog Santa Barbara CA 93106 USA Arizona State Univ Sch Geog Sci & Urban Planning Tempe AZ 85287 USA Univ Aegean Dept Geog Mitilini Greece

Arid and semi-arid shrublands have significant biological and economical values and have been experiencing dramatic changes due to human activities. In California, California sage scrub (CSS) is one of the most endangered plant communities in the US and requires close monitoring in order to conserve this important biological resource. We investigate the utility of remote-sensing approaches-object-based image analysis applied to pansharpened QuickBird imagery (QBPS/OBIA) and multiple endmember spectral mixture analysis (MESMA) applied to SPOT imagery (SPOT/MESMA)-for estimating fractional cover of true shrub, subshrub, herb, and bare ground within CSS communities of southern California. We also explore the effectiveness of life-form cover maps for assessing CSS conditions. Overall and combined shrub cover (i.e., true shrub and subshrub) were estimated more accurately using QBPS/OBIA (mean absolute error or MAE, 8.9 %) than SPOT/MESMA (MAE, 11.4 %). Life-form cover from QBPS/OBIA at a 25 x 25 m grid cell size seems most desirable for assessing CSS because of its higher accuracy and spatial detail in cover estimates and amenability to extracting other vegetation information (e.g., size, shape, and density of shrub patches). Maps derived from SPOT/MESMA at a 50 x 50 m scale are effective for retrospective analysis of life-form cover change because their comparable accuracies to QBPS/OBIA and availability of SPOT archives data dating back to the mid-1980s. The framework in this study can be applied to other physiognomically comparable shrubland communities.

关键词： Remote sensing Habitat monitoring Shrublands Mixture models object-based image analysis

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object based image analysis for the classification of the growth stages of Avocado crop, in Michoacan State, Mexico 5

Object based image analysis for the classification of the gr...

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Conference on Multispectral, Hyperspectral, and Ultraspectral Remote Sensing Technology, Techniques and Applications V

作者： Gao, Yan Marpu, Prashanth Morales Manilla, Luis M. UNAM Ctr Invest Geog Ambiental Antigua Carretera Patzcuaro 8701 Morelia 58190 Michoacan Mexico Masdar Inst Sci & Technol Abu Dhabi U Arab Emirates

ISBN: (纸本)9781628413304

This paper assesses the suitability of 8-band Worldview-2 (WV2) satellite data and object-based random forest algorithm for the classification of avocado growth stages in Mexico. We tested both pixel-based with minimum distance (MD) and maximum likelihood (MLC) and object-based with Random Forest (RF) algorithm for this task. Training samples and verification data were selected by visual interpreting the WV2 images for seven thematic classes: fully grown, middle stage, and early stage of avocado crops, bare land, two types of natural forests, and water body. To examine the contribution of the four new spectral bands of WV2 sensor, all the tested classifications were carried out with and without the four new spectral bands Classification accuracy assessment results show that object-based classification with RF algorithm obtained higher overall higher accuracy (93.06%) than pixel-based MD (69.37%) and MLC (64.03%) method. For both pixel-based and object-based methods, the classifications with the four new spectral bands (overall accuracy obtained higher accuracy than those without: overall accuracy of object-based RF classification with vs without: 93.06% vs 83.59%, pixel-based MD: 69.37% vs 67.2%, pixel-based MLC: 64.03% vs 36.05%, suggesting that the four new spectral bands in WV2 sensor contributed to the increase of the classification accuracy.

关键词： Avocado crop image segmentation object-based image analysis WorldView-2 Pixel-based image classification

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Evaluating pixel-based ***-based image analysis approaches for lithological discrimination using VNIR data of WorldView-3

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Frontiers of Earth Science 2021年第1期15卷 38-53页

作者： Samira SHAYEGANPOUR Majid H.TANGESTANI Saeid HOMAYOUNI Robert K.VINCENT Department of Earth Sciences Faculty of SciencesShiraz UniversityShiraz ***Iran Centre Eau Terre Environnement Insitut National de la Recherche ScientifiqueQuebec QCJ3X1S2Canada Department of Geography Bowling Green State UniversityOhio 43403-0001USA

The object-based against pixel-based image analysis approaches were assessed for lithological mapping in a geologically complex terrain using Visible Near Infrared(VNIR)bands of WorldView-3(WV-3)satellite *** study area is Hormuz Island,southern Iran,a salt dome composed of dominant sedimentary and igneous *** performing the object-based image analysis(OBLA)approach,the textural and spectral characteristics of lithological features were analyzed by the use of support vector machine(SVM)***,in the pixelbased image analysis(PBIA),the spectra of lithological end-members,extracted from imagery,were used through the spectral angle mapper(SAM)*** test samples were used in a confusion matrix to assess the accuracy of classification methods *** showed that OBIA was capable of lithological mapping with an overall accuracy of 86.54%which was 19.33%greater than the accuracy of *** also reduced the salt-and-pepper artifact pixels and produced a more realistic map with sharper lithological *** research showed limitations of pixel-based method due to relying merely on the spectral characteristics of rock types when applied to high-spatial-resolution VNIR bands of WorldView-3 *** is concluded that the application of an object-based image analysis approach obtains a more accurate lithological classification when compared to a pixel-based image analysis algorithm.

关键词： object-based image analysis pixel-based image analysis lithological mapping Worldview-3 Hormuz Island spectral angle mapper support vector machine

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Advancing Seabed Bedform Mapping in the Kuźnica Deep: Leveraging Multibeam Echosounders and Machine Learning for Enhanced Underwater Landscape analysis

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REMOTE SENSING 2025年第3期17卷 373-373页

作者： Janowski, Lukasz Gdynia Maritime Univ Maritime Inst Roberta De Plelo 20 PL-80548 Gdansk Poland

The ocean, covering 71% of Earth's surface, remains largely unexplored due to the challenges of the marine environment. This study focuses on the Ku & zacute;nica Deep in the Baltic Sea, aiming to develop an automatic seabed mapping methodology using multibeam echosounders (MBESs) and machine learning. The research integrates various scientific fields to enhance understanding of the Ku & zacute;nica Deep's underwater landscape, addressing sediment composition, backscatter intensity, and geomorphometric features. Advances in remote sensing, particularly, object-based image analysis (OBIA) and machine learning, have significantly improved geospatial data analysis for underwater landscapes. The study highlights the importance of using a reduced set of relevant features for training models, as identified by the Boruta algorithm, to improve accuracy and robustness. Key geomorphometric features were crucial for seafloor composition mapping, while textural features were less significant. The study found that models with fewer, carefully selected features performed better, reducing overfitting and computational complexity. The findings support hydrographic, ecological, and geological research by providing reliable seabed composition maps and enhancing decision-making and hypothesis generation.

关键词： underwater remote sensing seabed mapping multibeam echosounder object-based image analysis machine learning underwater landscape geomorphological bedforms Puck Lagoon Ku & zacute nica Deep

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Mapping waste piles in an urban environment using ground surveys, manual digitization of drone imagery, and object based image classification approach

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ENVIRONMENTAL MONITORING AND ASSESSMENT 2025年第4期197卷 1-16页

作者： Kalonde, Patrick K. Mwapasa, Taonga Mthawanji, Rosheen Chidziwisano, Kondwani Morse, Tracy Torguson, Jeffrey S. Jones, Christopher M. Quilliam, Richard S. Feasey, Nicholas A. Henrion, Marc Y. R. Stanton, Michelle C. Blinnikov, Mikhail S. St Cloud State Univ Dept Geog & Planning 720 4th Ave South St Cloud MN 56301 USA Univ Liverpool Liverpool Sch Trop Med LIVERPOOL England Malawi Liverpool Wellcome Programme POB 30096 Blantyre Malawi Malawi Univ Business & Appl Sci Ctr Water Sanitat Hlth & Appropriate Technol Dev W Blantyre Malawi Univ Strathclyde Dept Civil & Environm Engn Glasgow Scotland Univ Stirling Fac Nat Sci Biol & Environm Sci Stirling FK9 4LA Scotland Univ St Andrews Sch Med St Andrews Scotland

There is wide recognition of the threats posed by the open dumping of waste in the environment. However, tools to surveil interventions for reducing this practice are poorly developed. This study explores the use of drone imagery for environmental surveillance. Drone images of waste piles were captured in a densely populated residential neighborhood in the Republic of Malawi. images were processed using the Structure for Motion (SfM) technique and partitioned into segments using Orfeo Toolbox mounted in QGIS software. A total of 509 segments were manually labeled to generate data for training and testing a series of classification models. Four supervised classification algorithms (Random Forest, Artificial Neural Network, Na & iuml;ve Bayes, and Support Vector Machine) were trained, and their performances were assessed regarding precision, recall, and F-1 score. Ground surveys were also conducted to map waste piles using a Global Positioning System (GPS) receiver and determine the physical composition of materials on the waste pile surface. Differences were observed between the field survey done by community-led physical mapping of waste piles and drone mapping. Drone mapping identified more waste piles than field surveys, and the spatial extent of waste piles was computed for each waste pile. The binary Support Vector Machine model predictions were the highest performing, with a precision of 0.98, recall of 0.99, and F1-score of 0.98. Drone mapping enabled the identification of waste piles in areas that cannot be accessed during ground surveys and further allowed the quantification of the total land surface area covered by waste piles. Drone imagery-based surveillance of waste piles thus has the potential to guide environmental waste policy, offer solutions for permanent monitoring, and evaluate waste reduction interventions.

关键词： Waste pile mapping object-based image analysis Orfeo Toolbox Environmental monitoring Low-income countries Waste management

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Mapping urban green structures using object-based analysis of satellite imagery: A review

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ECOLOGICAL INDICATORS 2025年 170卷

作者： Karan, Shivesh Kishore Borchsenius, Bjorn Tobias Debella-Gilo, Misganu Rizzi, Jonathan Norwegian Inst Bioecon Res Dept Geomat As Norway Norwegian Univ Life Sci Dept Geomat As Norway

Urban green structures (UGS) play important roles in enhancing urban ecosystems by providing benefits such as mitigating the urban heat island effect, improving air quality, supporting biodiversity, and aiding in stormwater management. Accurately mapping UGS is important for sustainable urban planning and management. Traditional methods of mapping such as manual mapping, aerial photography interpretation and pixel-based classification have limitations in terms of coverage, accuracy, and efficiency. object-based image analysis (OBIA) has gained prominence due to its ability to incorporate both spectral and spatial information making it particularly effective for classification of high-resolution satellite data. This paper reviews the application of OBIA on satellite images for UGS mapping, focusing on various data sources, popular segmentation methods, and classification techniques, highlighting their respective advantages and limitations. Key segmentation methodologies discussed include multi-resolution segmentation and watershed segmentation. For classification, the review covers machine learning techniques such as random forests, support vector machines, and convolutional neural networks, among others. Several case studies highlight the successful implementation of OBIA in diverse urban environments by demonstrating improvements in classification accuracy and detail. The review also addresses the challenges associated with OBIA, such as dealing with heterogenous urban landscapes, data sources and with OBIA methods itself. Future directions for UGS mapping include the integration of deep learning algorithms, advancements in satellite data technologies, and the development of standardized classification frameworks. By providing a detailed analysis of the current state-of-the-art in object-based UGS mapping, this review aims to guide future research and practical applications in UGS management.

关键词： Classification object-based image analysis Satellite imagery Segmentation Urban Green Structure Urban Planning

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Landslide mapping using a combination of sentinel-2 multi spectral instruments and GIS data at Namwon, Jeollabuk-do, South Korea

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GEOMECHANICS AND ENGINEERING 2024年第4期39卷 385-396页

作者： Lim, Hwan-Hui Lee, Seung-Min Cheon, Enok Song, Eu Jeon, Jun-Seo Lee, Seung-Rae K water Res Inst Water Infrastruct Res Ctr Daejeon 34141 South Korea Republ Korea Armed Forces 21st Infantry Div Seoul South Korea Natl Inst Forest Sci Dept Forest Environm & Conservat Landslide Team Seoul 02455 South Korea Korea Inst Civil Engn & Bldg Technol Dept Geotech Engn Res Goyang Si 10223 South Korea

With the recent development of satellite, aerial, and remote sensing technologies, it is easy to produce landslide inventory maps over a large area. In this study, the object-based framework was designed to address the limitations inherent in the pixel-based deep learning (DL) methodology. This framework explores the potential of combining Sentinel-2 MultiSpectral Instrument (MSI) satellite imagery and digital elevation models (DEMs) to enhance shallow landslide mapping across diverse terrains comprehensively. The study area for analysis and verification was selected as Jucheon-myeon, Namwon-si, and Jeollabuk-do, where significant large-scale landslides and slope failures occurred in 2020. As a result, the application of this framework led to the classification of 68 candidate sites spanning an area of 0.5 hectares or more. Site surveying was conducted on 20 random sites with a 1ha or more scale. Furthermore, six sites were selected where satellite imagery could discern the damaged areas. At these locations, the damaged area estimated by the framework was compared with the actual observed damaged area to assess accuracy. These rapid and cost-effective landslide mapping techniques can accurately estimate the location and extent of landslides and enhance the precision of sensitivity models and land management strategies.

关键词： image classification inventory map landslide object-based image analysis satellite

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Inventorying ponds through novel size-adaptive object mapping using Sentinel-1/2 time series

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REMOTE SENSING OF ENVIRONMENT 2024年 315卷

作者： Liu, Denghong Zhu, Xiaolin Holgerson, Meredith Bansal, Sheel Xu, Xiangtao Hong Kong Polytech Univ Dept Land Surveying & Geoinformat Hong Kong Peoples R China Cornell Univ Ecol & Evolutionary Biol Ithaca NY 14850 USA Northern Prairie Wildlife Res Ctr US Geol Survey Jamestown ND USA

Ponds are an important source of greenhouse gases (GHGs) to the atmosphere, yet evaluating their role in global biogeochemical cycling is currently hampered by limitations in quantifying their global distribution. Existing satellite-derived estimates of lake distributions have difficulty identifying small lakes (5-10 ha) and ponds (<5 ha) due to limitations in satellite resolution and challenges extracting individual small waterbodies from low-albedo surfaces, vegetated water, and lotic water systems including rivers and streams. In this study, we developed generalizable pond mapping strategies based on their spatial-temporal-spectral characteristics to fully exploit accessible medium-resolution optical and synthetic aperture radar (SAR) time series to identify ponds. Our novel approach entails: (1) making full use of ponds' characteristics from an object-based perspective;(2) extracting pond objects using seeds of prominent water pixels defined by the SAR VH signal;(3) constructing training samples of ponds with high representativeness;and (4) improving inter-class discrimination by combining features from optical and SAR data. We designed a novel Optical-SAR Pond object Mapper (OptiSAR-POM) to achieve an improved estimate of pond size distribution by incorporating mapping strategies into the object-based image analysis framework. We generated landscape objects through an elaborate water-focused segmentation approach, which adaptively aligned the segmentation parameters with the size and distribution patterns of ponds to identify small waterbodies and increase inter-class variability. We further introduced an interactive learning process to construct random forests for object-based classification, which incorporated adaptive empirical thresholds to identify potential pond objects and select representative training samples of varying sizes. We tested the OptiSAR-POM framework using Sentinel-1/2 time series at three county-level study sites and three supplementary wa

关键词： Ponds Optical-SAR Mapping object-based image analysis Sentinel-1/2 imagery

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